Event-triggered adaptive deep neural network sliding mode control design for unmanned aerial vehicle systems

Abstract

This paper proposes an event-triggered adaptive attitude maneuver control strategy based on deep neural network (DNN) for quadcopters to improve the attitude tracking performance and attenuate the effects of nonlinear and uncertainty. First, we establish an affine function model constructed to describe the dynamic model of quadcopters. The DNN trained over a large time scale is used to approximate the nonlinearity and uncertainty. Then, an adaptive sliding mode control (SMC) is combined with the DNN to achieve accurate tracking of attitude trajectory. Furthermore, to reduce unnecessary data transmissions, an event-trigger mechanism is combined with the designed DNN-driven adaptive SMC. The Lyapunov-based stability analysis is used to prove the stability of the closed loop system. The simulation results show the good performance of the proposed control strategy.